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https://github.com/jannisborn/pyacddb
Python parser of ACDDB files (ACDSee)
https://github.com/jannisborn/pyacddb
Last synced: 16 days ago
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Python parser of ACDDB files (ACDSee)
- Host: GitHub
- URL: https://github.com/jannisborn/pyacddb
- Owner: jannisborn
- License: mit
- Created: 2024-03-04T22:26:17.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2024-08-24T14:06:42.000Z (4 months ago)
- Last Synced: 2024-12-07T12:30:02.869Z (16 days ago)
- Language: Python
- Size: 229 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# pyacddb
## What is this
`pyacddb` is a Python package designed for parsing and extracting data from ACDSee-generated XML files.
Such files have headers like `` and store metadata about digital assets.
`pyacddb` performs streamlined data manipulation and analysis. It extracts, normalize, and analyze metadata from their ACDSee digital asset management system.
## Installation
Follow these steps to install `pyacddb` using Poetry, ensuring seamless management of dependencies:
1. Install Poetry if it's not already set up on your system. You can find installation instructions on the [Poetry website](https://python-poetry.org/docs/).
2. Clone the `pyacddb` repository:
```bash
git clone https://github.com/yourusername/pyacddb.git
cd pyacddb
poetry install
```
## Usage
To get started with `pyacddb`, here's a simple guide:
```py
from pyacddb.core import extract_keywords
# Specify the path to your ACDSee XML file
xml_file_path = 'path/to/your/data.xml'
# Define the list of keywords (XML tags) you are interested in
keywords = ["Name", "Folder", "FileType", "ImageType", "DBDate", "Caption", "Author"]
# Extract the data into a DataFrame, filtering by your specified keywords
data_df = extract_keywords(xml_file_path, keywords)
# Save the DataFrame to a CSV file for further use or analysis
data_df.to_csv("filtered_output_data.csv", encoding="utf-8-sig")
```
## Features
- Tailored parsing of ACDSee-generated XML files, ensuring accurate metadata extraction.
- Data normalization includes handling special characters
- Utilizes Pandas for efficient data storage and manipulation, catering to diverse data analysis needs.
## License
`pyacddb` is open-sourced software licensed under the MIT License. For more details, see the LICENSE file.